Hydrogen Diffusion and Trapping in α-Iron: The Role of Quantum and Anharmonic Fluctuations

Ab initiopath-integral simulations of hydrogen-isotope diffusion in face-centred cubic metals

Lighter isotopes typically diffuse faster than heavier isotopes; however, the case is not necessarily true for H. Predicting the kinetics of H isotope transport and reactions in substances remains a fundamental challenge in material and condensed matter physics. The peculiar experimentally observed isotope effect on H diffusivities in face-centred cubic (fcc) metals has long been an unresolved problem. Using anab initiopath-integral approach to explore the quantum mechanical nature of both electrons and nuclei, this study successfully predicts H isotope diffusivities in fcc Pd over a wide temperature range. The temperature dependence of the diffusivities follows an unusual 'reversed-S' shape on Arrhenius plots. This irregular behaviour, arising from the competition between different nuclear quantum effects (NQEs) with different temperature dependencies, reveals the mechanism of anomalous crossovers between normal and reversed isotope effects. The results illustrate that this phenomenon is common in other fcc metals (e.g. Cu and Ag), where H atoms prefer to occupy octahedral (O) sites. Conversely, in Al, where H atoms prefer to occupy tetrahedral (T) sites, the dependence of H diffusivities on temperature exhibits a familiar 'C' shape. A lattice expansion of approximately 1%-2% causes the stable position of H atoms dissolved in Pd to shift from the O to T sites, and H diffusion in expanded Pd is no longer suppressed by NQEs, as observed in Al. This finding has important implications for interpreting kinetic processes involving the crossover from classical to quantum behaviour of H atoms moving between different interstitial sites. Path-integral simulation results describing the approximate quantum dynamics of the Pd-H system, using a machine-learning-based interatomic potential with accuracy similar to the density functional theory calculations, are presented. This computational approach paves the way for elucidating the quantum behaviour of H isotopes in various materials.

Hydrogen Embrittlement as a Conspicuous Material Challenge─Comprehensive Review and Future Directions

Hydrogen is considered a clean and efficient energy carrier crucial for shaping the net-zero future. Large-scale production, transportation, storage, and use of green hydrogen are expected to be undertaken in the coming decades. As the smallest element in the universe, however, hydrogen can adsorb on, diffuse into, and interact with many metallic materials, degrading their mechanical properties. This multifaceted phenomenon is generically categorized as hydrogen embrittlement (HE). HE is one of the most complex material problems that arises as an outcome of the intricate interplay across specific spatial and temporal scales between the mechanical driving force and the material resistance fingerprinted by the microstructures and subsequently weakened by the presence of hydrogen. Based on recent developments in the field as well as our collective understanding, this Review is devoted to treating HE as a whole and providing a constructive and systematic discussion on hydrogen entry, diffusion, trapping, hydrogen-microstructure interaction mechanisms, and consequences of HE in steels, nickel alloys, and aluminum alloys used for energy transport and storage. HE in emerging material systems, such as high entropy alloys and additively manufactured materials, is also discussed. Priority has been particularly given to these less understood aspects. Combining perspectives of materials chemistry, materials science, mechanics, and artificial intelligence, this Review aspires to present a comprehensive and impartial viewpoint on the existing knowledge and conclude with our forecasts of various paths forward meant to fuel the exploration of future research regarding hydrogen-induced material challenges.

A streamlined molecular-dynamics workflow for computing solubilities of molecular and ionic crystals

Computing the solubility of crystals in a solvent using atomistic simulations is notoriously challenging due to the complexities and convergence issues associated with free-energy methods, as well as the slow equilibration in direct-coexistence simulations. This paper introduces a molecular-dynamics workflow that simplifies and robustly computes the solubility of molecular or ionic crystals. This method is considerably more straightforward than the state-of-the-art, as we have streamlined and optimised each step of the process. Specifically, we calculate the chemical potential of the crystal using the gas-phase molecule as a reference state, and employ the S0 method to determine the concentration dependence of the chemical potential of the solute. We use this workflow to predict the solubilities of sodium chloride in water, urea polymorphs in water, and paracetamol polymorphs in both water and ethanol. Our findings indicate that the predicted solubility is sensitive to the chosen potential energy surface. Furthermore, we note that the harmonic approximation often fails for both molecular crystals and gas molecules at or above room temperature, and that the assumption of an ideal solution becomes less valid for highly soluble substances.

Quantitative tests revealing hydrogen-enhanced dislocation motion in α-iron

Hydrogen embrittlement jeopardizes the use of high-strength steels in critical load-bearing applications. However, uncertainty regarding how hydrogen affects dislocation motion, owing to the lack of quantitative experimental evidence, hinders our understanding of hydrogen embrittlement. Here, by studying the well-controlled, cyclic, bow-out motions of individual screw dislocations in α-iron, we find that the critical stress for initiating dislocation motion in a 2 Pa electron-beam-excited H₂ atmosphere is 27-43% lower than that in a vacuum environment, proving that hydrogen enhances screw dislocation motion. Moreover, we find that aside from vacuum degassing, cyclic loading and unloading facilitates the de-trapping of hydrogen, allowing the dislocation to regain its hydrogen-free behaviour. These findings at the individual dislocation level can inform hydrogen embrittlement modelling and guide the design of hydrogen-resistant steels.

Phase diagrams-Why they matter and how to predict them

Understanding the thermodynamic stability and metastability of materials can help us to, for example, gauge whether crystalline polymorphs in pharmaceutical formulations are likely to be durable. It can also help us to design experimental routes to novel phases with potentially interesting properties. In this Perspective, we provide an overview of how thermodynamic phase behavior can be quantified both in computer simulations and machine-learning approaches to determine phase diagrams, as well as combinations of the two. We review the basic workflow of free-energy computations for condensed phases, including some practical implementation advice, ranging from the Frenkel-Ladd approach to thermodynamic integration and to direct-coexistence simulations. We illustrate the applications of such methods on a range of systems from materials chemistry to biological phase separation. Finally, we outline some challenges, questions, and practical applications of phase-diagram determination which we believe are likely to be possible to address in the near future using such state-of-the-art free-energy calculations, which may provide fundamental insight into separation processes using multicomponent solvents.

Thermodynamics of high-pressure ice phases explored with atomistic simulations

Most experimentally known high-pressure ice phases have a body-centred cubic (bcc) oxygen lattice. Our large-scale molecular-dynamics simulations with a machine-learning potential indicate that, amongst these bcc ice phases, ices VII, VII′ and X are the same thermodynamic phase under different conditions, whereas superionic ice VII″ has a first-order phase boundary with ice VII′. Moreover, at about 300 GPa, the transformation between ice X and the Pbcm phase has a sharp structural change but no apparent activation barrier, whilst at higher pressures the barrier gradually increases. Our study thus clarifies the phase behaviour of the high-pressure ices and reveals peculiar solid–solid transition mechanisms not known in other systems.

Many experimentally known high-pressure ice phase are structurally very similar. Here authors elucidate the phase behaviour of the high-pressure insulating ices and reveal solid-solid transition mechanisms not known in other systems.

Quantum-mechanical exploration of the phase diagram of water

The set of known stable phases of water may not be complete, and some of the phase boundaries between them are fuzzy. Starting from liquid water and a comprehensive set of 50 ice structures, we compute the phase diagram at three hybrid density-functional-theory levels of approximation, accounting for thermal and nuclear fluctuations as well as proton disorder. Such calculations are only made tractable because we combine machine-learning methods and advanced free-energy techniques. The computed phase diagram is in qualitative agreement with experiment, particularly at pressures ≲ 8000 bar, and the discrepancy in chemical potential is comparable with the subtle uncertainties introduced by proton disorder and the spread between the three hybrid functionals. None of the hypothetical ice phases considered is thermodynamically stable in our calculations, suggesting the completeness of the experimental water phase diagram in the region considered. Our work demonstrates the feasibility of predicting the phase diagram of a polymorphic system from first principles and provides a thermodynamic way of testing the limits of quantum-mechanical calculations.

Identification of synthesisable crystalline phases of water – a prototype for the challenges of computational materials design

We discuss the identification of experimentally realisable crystalline phases of water to outline and contextualise some of the diverse building blocks of a computational materials design process.

Computing the Heat Conductivity of Fluids from Density Fluctuations

Equilibrium molecular dynamics simulations, in combination with the Green-Kubo (GK) method, have been extensively used to compute the thermal conductivity of liquids. However, the GK method relies on an ambiguous definition of the microscopic heat flux, which depends on how one chooses to distribute energies over atoms. This ambiguity makes it problematic to employ the GK method for systems with nonpairwise interactions. In this work, we show that the hydrodynamic description of thermally driven density fluctuations can be used to obtain the thermal conductivity of a bulk fluid unambiguously, thereby bypassing the need to define the heat flux. We verify that, for a model fluid with only pairwise interactions, our method yields estimates of thermal conductivity consistent with the GK approach. We apply our approach to compute the thermal conductivity of a nonpairwise additive water model at supercritical conditions, and of a liquid hydrogen system described by a machine-learning interatomic potential, at 33 GPa and 2000 K.

Ab initio thermodynamics of liquid and solid water

Thermodynamic properties of liquid water as well as hexagonal (Ih) and cubic (Ic) ice are predicted based on density functional theory at the hybrid-functional level, rigorously taking into account quantum nuclear motion, anharmonic fluctuations, and proton disorder. This is made possible by combining advanced free-energy methods and state-of-the-art machine-learning techniques. The ab initio description leads to structural properties in excellent agreement with experiments and reliable estimates of the melting points of light and heavy water. We observe that nuclear-quantum effects contribute a crucial [Formula: see text] to the stability of ice Ih, making it more stable than ice Ic. Our computational approach is general and transferable, providing a comprehensive framework for quantitative predictions of ab initio thermodynamic properties using machine-learning potentials as an intermediate step.